I recently stumbled on a Yale paper from A. J. Wasserstein et al that analyzed 50+ Search Fund deals with a combined EV of ~$4bn. The main observation is that 80% of Enterprise Value Creation is from EBITDA Multiple Expansion while the remainder 20% is from Revenue Growth / EBITDA Margin Expansion. My reflections: 1. The fundamentals still hold: buy low, sell high. For Search Funds this will translate loosely to find a great business at a great price in a growing industry so you can exit at a great multiple. 2. Don't get overwhelmed by AI theater: analyze your deal and focus on what drives the most value. For the LMM PE/Search Fund use cases, AI like other digital transformations before it, will primarily impact serving more customers (i.e. Revenue Growth) and optimizing your cost (i.e. EBITDA Margin Expansion) which together will only impact 20% of your EV Creation. You should channel your attention and resources accordingly. If you are reviewing a Search Fund deal, you can map the drivers of your EV Creation within 5 minutes using the proprietary solver of Fontics, an Excel plugin I built for PE deal analysis. This does not exist in any other tool and or Claude. Fig: EV Creation comes from EBITDA Multiple Expansion (80%), EBITDA Expansion (20%) Diving deeper: Entry Multiple: ~50%, Exit Multiple: ~30%, Revenue Growth ~15%, Margin Expansion, ~5%redacted If you are a financial advisor, investor, lender, searcher etc and want to apply this to your transaction analysis, you can self-serve in 5 minutes. If you want to chat more or want a 1:1 demo, my DM is open. And by the way, the plan for LMM/Search Fund deals is $25/month. Before you spend weeks on skills, or thousands on AI transformation for your deal, or spend weeks on DD, spend 5 minutes to answer the question: what are the real drivers of my return and how realistic are my assumptions of achieving them?